Annual bacterial community cycle in a seasonally ice‐covered river reflects environmental and climatic conditions
Bibliographic record
Abstract
Abstract Bacteria are key players in biogeochemical cycles and control water quality in freshwater ecosystems. Nevertheless, little is known about the identity and ecology of riverine bacteria, especially during ice‐covered periods that are often mistakenly perceived as periods with negligible biological activities. Here, we analyzed in detail the effects of environmental and climatic conditions on freshwater bacterial community structure and diversity over a 2‐yr sampling campaign, targeting a seasonally ice‐covered river of the Quebec City (Canada) area, the Saint‐Charles River. Quantitative polymerase chain reaction and 16S ribosomal RNA gene high‐throughput sequencing demonstrated a strong seasonal cycle of the bacterial community composition with rapid successions of bacterial lineages, reflecting the harsh climatic condition of the region. During the summer, the bacterial community was dominated by typical freshwater microorganisms such as Limnohabitans, Sporichthyaceae hgcI/acI clade, and Pseudarcicella. In contrast, the results suggest that during the cold season, the low water temperatures, combined with other prevailing conditions such as reduced light availability and minimal particulate inputs from the catchment, created various environmental niches for potential methanotrophic Gammaproteobacteria, such as Crenothrix and Methylobacter, other Betaproteobacteria, such as Candidatus Methylopumilus, Candidatus Nitrotoga, and Rhodoferax, as well as Verrucomicrobia and Parcubacteria. The presence of these taxa in the winter suggests active carbon, iron, and nitrogen cycling under ice, whereas summer lineages are dormant or in a phase of reduced activity. These results increase our understanding of bacterial dynamic and potential metabolic processes occurring in seasonally ice‐covered inland waters, providing evidence that winter can be an especially important period for freshwater ecological processes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".